管理科学12 决策分析解析课件.ppt
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1、Chapter 12 - Decision Analysis,1,Chapter 12Decision Analysis,Introduction to Management Science8th EditionbyBernard W. Taylor III,Chapter 12 - Decision Analysis,2,Components of Decision MakingDecision Making without ProbabilitiesDecision Making with ProbabilitiesDecision Analysis with Additional Inf
2、ormationUtility,Chapter Topics,Chapter 12 - Decision Analysis,3,Table 12.1Payoff Table,A state of nature is an actual event that may occur in the future.A payoff table is a means of organizing a decision situation, presenting the payoffs from different decisions given the various states of nature.,D
3、ecision AnalysisComponents of Decision Making,Chapter 12 - Decision Analysis,4,Decision situation:Decision-Making Criteria: maximax, maximin, minimax, minimax regret, Hurwicz, and equal likelihood,Table 12.2Payoff Table for the Real Estate Investments,Decision AnalysisDecision Making without Probabi
4、lities,Chapter 12 - Decision Analysis,5,Table 12.3Payoff Table Illustrating a Maximax Decision,In the maximax criterion the decision maker selects the decision that will result in the maximum of maximum payoffs; an optimistic criterion.,Decision Making without ProbabilitiesMaximax Criterion,Chapter
5、12 - Decision Analysis,6,Table 12.4Payoff Table Illustrating a Maximin Decision,In the maximin criterion the decision maker selects the decision that will reflect the maximum of the minimum payoffs; a pessimistic criterion.,Decision Making without ProbabilitiesMaximin Criterion,Chapter 12 - Decision
6、 Analysis,7,Table 12.6 Regret Table Illustrating the Minimax Regret Decision,Regret is the difference between the payoff from the best decision and all other decision payoffs.The decision maker attempts to avoid regret by selecting the decision alternative that minimizes the maximum regret.,Decision
7、 Making without ProbabilitiesMinimax Regret Criterion,Chapter 12 - Decision Analysis,8,The Hurwicz criterion is a compromise between the maximax and maximin criterion.A coefficient of optimism, , is a measure of the decision makers optimism.The Hurwicz criterion multiplies the best payoff by and the
8、 worst payoff by 1- ., for each decision, and the best result is selected.Decision ValuesApartment building $50,000(.4) + 30,000(.6) = 38,000Office building $100,000(.4) - 40,000(.6) = 16,000Warehouse $30,000(.4) + 10,000(.6) = 18,000,Decision Making without ProbabilitiesHurwicz Criterion,Chapter 12
9、 - Decision Analysis,9,The equal likelihood ( or Laplace) criterion multiplies the decision payoff for each state of nature by an equal weight, thus assuming that the states of nature are equally likely to occur. Decision ValuesApartment building $50,000(.5) + 30,000(.5) = 40,000Office building $100
10、,000(.5) - 40,000(.5) = 30,000Warehouse $30,000(.5) + 10,000(.5) = 20,000,Decision Making without ProbabilitiesEqual Likelihood Criterion,Chapter 12 - Decision Analysis,10,A dominant decision is one that has a better payoff than another decision under each state of nature.The appropriate criterion i
11、s dependent on the “risk” personality and philosophy of the decision maker. Criterion Decision (Purchase)MaximaxOffice buildingMaximinApartment buildingMinimax regretApartment buildingHurwiczApartment buildingEqual likelihoodApartment building,Decision Making without ProbabilitiesSummary of Criteria
12、 Results,Chapter 12 - Decision Analysis,11,Exhibit 12.1,Decision Making without ProbabilitiesSolution with QM for Windows (1 of 3),Chapter 12 - Decision Analysis,12,Exhibit 12.2,Decision Making without ProbabilitiesSolution with QM for Windows (2 of 3),Chapter 12 - Decision Analysis,13,Exhibit 12.3,
13、Decision Making without ProbabilitiesSolution with QM for Windows (3 of 3),Chapter 12 - Decision Analysis,14,Expected value is computed by multiplying each decision outcome under each state of nature by the probability of its occurrence.EV(Apartment) = $50,000(.6) + 30,000(.4) = 42,000EV(Office) = $
14、100,000(.6) - 40,000(.4) = 44,000EV(Warehouse) = $30,000(.6) + 10,000(.4) = 22,000,Table 12.7Payoff table with Probabilities for States of Nature,Decision Making with ProbabilitiesExpected Value,Chapter 12 - Decision Analysis,15,The expected opportunity loss is the expected value of the regret for e
15、ach decision.The expected value and expected opportunity loss criterion result in the same decision.EOL(Apartment) = $50,000(.6) + 0(.4) = 30,000EOL(Office) = $0(.6) + 70,000(.4) = 28,000EOL(Warehouse) = $70,000(.6) + 20,000(.4) = 50,000,Table 12.8Regret (Opportunity Loss) Table with Probabilities f
16、or States of Nature,Decision Making with ProbabilitiesExpected Opportunity Loss,Chapter 12 - Decision Analysis,16,Exhibit 12.4,Expected Value ProblemsSolution with QM for Windows,Chapter 12 - Decision Analysis,17,Exhibit 12.5,Expected Value ProblemsSolution with Excel and Excel QM (1 of 2),Chapter 1
17、2 - Decision Analysis,18,Exhibit 12.6,Expected Value ProblemsSolution with Excel and Excel QM (2 of 2),Chapter 12 - Decision Analysis,19,The expected value of perfect information (EVPI) is the maximum amount a decision maker would pay for additional information.EVPI equals the expected value given p
18、erfect information minus the expected value without perfect information.EVPI equals the expected opportunity loss (EOL) for the best decision.,Decision Making with ProbabilitiesExpected Value of Perfect Information,Chapter 12 - Decision Analysis,20,Table 12.9Payoff Table with Decisions, Given Perfec
19、t Information,Decision Making with ProbabilitiesEVPI Example (1 of 2),Chapter 12 - Decision Analysis,21,Decision with perfect information:$100,000(.60) + 30,000(.40) = $72,000Decision without perfect information:EV(office) = $100,000(.60) - 40,000(.40) = $44,000EVPI = $72,000 - 44,000 = $28,000EOL(o
20、ffice) = $0(.60) + 70,000(.4) = $28,000,Decision Making with ProbabilitiesEVPI Example (2 of 2),Chapter 12 - Decision Analysis,22,Exhibit 12.7,Decision Making with ProbabilitiesEVPI with QM for Windows,Chapter 12 - Decision Analysis,23,A decision tree is a diagram consisting of decision nodes (repre
21、sented as squares), probability nodes (circles), and decision alternatives (branches).,Table 12.10Payoff Table for Real Estate Investment Example,Decision Making with ProbabilitiesDecision Trees (1 of 4),Chapter 12 - Decision Analysis,24,Figure 12.1Decision Tree for Real Estate Investment Example,De
22、cision Making with ProbabilitiesDecision Trees (2 of 4),Chapter 12 - Decision Analysis,25,The expected value is computed at each probability node: EV(node 2) = .60($50,000) + .40(30,000) = $42,000EV(node 3) = .60($100,000) + .40(-40,000) = $44,000EV(node 4) = .60($30,000) + .40(10,000) = $22,000Bran
23、ches with the greatest expected value are selected.,Decision Making with ProbabilitiesDecision Trees (3 of 4),Chapter 12 - Decision Analysis,26,Figure 12.2Decision Tree with Expected Value at Probability Nodes,Decision Making with ProbabilitiesDecision Trees (4 of 4),Chapter 12 - Decision Analysis,2
24、7,Exhibit 12.8,Decision Making with ProbabilitiesDecision Trees with QM for Windows,Chapter 12 - Decision Analysis,28,Exhibit 12.9,Decision Making with ProbabilitiesDecision Trees with Excel and TreePlan (1 of 4),Chapter 12 - Decision Analysis,29,Exhibit 12.10,Decision Making with ProbabilitiesDecis
25、ion Trees with Excel and TreePlan (2 of 4),Chapter 12 - Decision Analysis,30,Exhibit 12.11,Decision Making with ProbabilitiesDecision Trees with Excel and TreePlan (3 of 4),Chapter 12 - Decision Analysis,31,Exhibit 12.12,Decision Making with ProbabilitiesDecision Trees with Excel and TreePlan (4 of
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